“Contouring” refers to identifying and delineating objects or structures in images by way of contours corresponding to such objects/structures. Contouring is an important part of radiation therapy planning (RTP), wherein treatment plans are custom-designed for each patient's anatomy. Contours are often obtained “manually” in response to user input, wherein a user traces the object boundary on the image using a computer workstation's mouse and screen cursor. Contours can also be obtained via automated processes such as auto-thresholding programs and/or auto-segmentation programs. The process of delineating or “contouring” structures within an image is known in the field as “image segmentation.”
With the recent advent of “4D radiotherapy” or “adaptive radiotherapy”, the need for increased automation processes to generate contours for 3D medical images has grown greater. Four-dimensional (4D) and adaptive radiotherapy techniques were developed recently as an effort to improve the accuracy of dose deposition in the presence of organ and/or tumor motion, and also in situations where the organ and/or tumor change shape over the course of treatment. Instead of using a single 3D image for treatment planning, 4D radiotherapy planning uses a time-series of 3D images to model the motion and/or shape-change of structures. Consequently, the number of images involved in 4D radiotherapy is much greater than the number of images required for 3D radiotherapy.
Because structures shown in images are preferably identified in a computer-readable way to be useful for radiotherapy treatment, it is desirable that many 3D images be contoured. Due to the large number of images involved in 4D radiotherapy, manual contouring of these images is extremely tedious and time-consuming. Consequently, there is a strong need in the art for increased automation in the contouring of 3D medical images.
Several methods have been proposed, but these methods suffer from the drawbacks of long processing times and/or inaccurate contouring results. In an effort to solve these problems in the art, an exemplary embodiment of the present invention is configured to provide robust and accurate contouring results while also providing short processing times due to improved computational efficiency. This is achieved using a hybrid image registration and segmentation technique, as disclosed below.
In an exemplary embodiment of the present invention, a program is configured to generate 3D contours for a series of related target images with respect to an object of interest based on an initial reference image for which the object of interest has already been contoured. Preferably, the reference image has been contoured manually (for example, by a doctor using known software for manual input of contour data into a computer), but this need not be the case. When a segmentation technique relies on a manually-contoured reference image, it may be referred to as a “semi-automated” or “automatic re-contouring” method.
In addition to the contouring information which delineates the object of interest, it is desirable to know the correspondence between images in a series. Image registration is the process of determining the correspondence of points between two images collected at different times or using different imaging modalities. Image registration data, when combined with contouring data, provides valuable information about how structures such as an object of interest have moved or changed shape over time.
Current image processing software programs known to the inventors herein are believed to perform image registration and segmentation separately. In an exemplary embodiment, the inventors disclose an innovative technique for the automatic re-contouring of images based on simultaneous image registration and segmentation. By making use of pre-existing contouring data for the reference image, it is possible to provide improved accuracy in contouring the target image(s). Moreover, due to improved computational efficiency, an embodiment of the present invention is able to provide this improved accuracy while also providing shorter processing times.
When contouring a plurality of time-series images, it is believed that conventional contour-generating software ignores the contouring information available from computing the contouring information for one of these images when computing the image registration and segmentation of another image in the series. By taking into account the contouring information available with respect to the reference image, the inventors believe that the accuracy of re-contouring results can be improved.
In an embodiment of the present invention, the contouring information for the reference image is used as a starting point for an automated re-contouring process. Computer software can be used to calculate a surface displacement field. The surface displacement field defines the correspondence between points on the contour surface in the reference image and points on the new contour surface in the target image. The surface displacement field provides both the correspondence between the reference image and the target image (registration) and the location of objects of interest within the target image (segmentation). Therefore, it can be said that the calculation of the surface displacement field achieves a simultaneous image registration and segmentation. This surface displacement field preferably defines a vector field which can be used to map the contoured structures in the reference image onto the target image. Preferably, one surface displacement field is calculated for each object of interest in the reference image. The surface displacement field approach yields many advantages, as will be apparent from the detailed description below.
In an exemplary embodiment of the present invention, the surface displacement field is only computed at points of the already-delineated object boundaries in the reference image. Current contour-generating software is believed to redundantly compute the correspondence map for every point in the reference image during registrations. By computing the surface displacement field only at points on the already-delineated object boundaries, this embodiment achieves improved computational efficiency and shorter processing time. Thus, the inventors believe that traditional 4D re-contouring approaches use deformable image registration techniques which are formulated to answer the question: “What is the best match (in the target image) for every point in the reference image?” Instead, a preferred embodiment disclosed herein seeks to answer the question: “What is the best match (in the target image) for every point on the surface of the object desired to be found in the target image?” In providing an answer to this question, the preferred embodiment is believed to greatly enhance computational efficiency. While a preferred solution to the inventors' question as disclosed herein involves consideration of more points than just the object surface points from the reference contour (for example, an exemplary embodiment considers all points inside the object surface for histogram matching and also considers points in a neighborhood of the object surface for an image gradient computation), the inventors note that the total number of points considered with such processing will still only be a small part of the whole image; this is particularly true when contouring small objects such as a tumor.
In yet another exemplary embodiment of the present invention, a multi-resolution implementation is disclosed. In this embodiment, the surface displacement field is computed at multiple resolutions of each target image. First, the reference and target images are down-sampled to a coarse resolution. The surface displacement field is then calculated for this coarse resolution. Preferably, this “coarse-level” surface displacement field is used as the starting point for the surface displacement field calculation at the next finer resolution. Preferably, this process is repeated for a plurality of resolutions (e.g., three resolutions corresponding to coarse, medium, and fine (original) resolutions). By computing the surface displacement field at lower resolutions in this manner, the present invention is able to better capture large deformations in objects of interest.
While various advantages and features of several embodiments of the invention have been discussed above, a greater understanding of the invention including a fuller description of its other advantages and features may be attained by referring to the drawings and the detailed description of the preferred embodiment which follow.